可靠的深度扩散张量估计：重新思考数据驱动优化流程的能力
Reliable Deep Diffusion Tensor Estimation: Rethinking the Power of Data-Driven Optimization Routine
AI总结：
本文提出DoDTI方法，将加权线性最小二乘拟合与基于去噪的正则化相结合，并通过展开交替方向乘子法构建深度网络，在DTI参数估计中实现了高精度、强泛化和高效率。
摘要：
扩散张量成像（DTI）在临床诊断和神经科学研究中具有重要意义。然而，传统的基于模型的拟合方法往往对噪声敏感，导致DTI参数估计精度下降。传统的数据驱动深度学习方法虽然在精度和效率方面显示出潜力，但由于不同中心、扫描设备和研究之间使用的扫描协议多种多样，其对训练分布外数据的泛化能力有限，阻碍了更广泛的应用。本研究旨在应对这些挑战，并通过引入一种基于数据驱动优化的方法DoDTI来推动DTI的应用。DoDTI结合了加权线性最小二乘拟合算法和基于去噪的正则化技术。前者将来自不同采集设置的扩散加权（DW）图像拟合为扩散张量场，而后者应用基于深度学习的去噪器对扩散张量场而非DW图像进行正则化，从而摆脱了网络固定通道分配的限制。优化目标通过交替方向乘子法求解，然后展开构建深度神经网络，利用数据驱动策略学习网络参数。利用内部模拟数据集和外部获取的活体数据集进行了广泛的验证实验。包括定性和定量分析在内的结果表明，所提出的方法在DTI参数估计方面达到了最先进的性能。值得注意的是，它展示了优越的泛化能力、精度和效率，使其在该领域的广泛应用中具有高度可靠性。
英文摘要：
Diffusion tensor imaging (DTI) holds significant importance in clinical diagnosis and neuroscience research. However, conventional model-based fitting methods often suffer from sensitivity to noise, leading to decreased accuracy in estimating DTI parameters. While traditional data-driven deep learning methods have shown potential in terms of accuracy and efficiency, their limited generalization to out-of-training-distribution data impedes their broader application due to the diverse scan protocols used across centers, scanners, and studies. This work aims to tackle these challenges and promote the use of DTI by introducing a data-driven optimization-based method termed DoDTI. DoDTI combines the weighted linear least squares fitting algorithm and regularization by denoising technique. The former fits DW images from diverse acquisition settings into diffusion tensor field, while the latter applies a deep learning-based denoiser to regularize the diffusion tensor field instead of the DW images, which is free from the limitation of fixed-channel assignment of the network. The optimization object is solved using the alternating direction method of multipliers and then unrolled to construct a deep neural network, leveraging a data-driven strategy to learn network parameters. Extensive validation experiments are conducted utilizing both internally simulated datasets and externally obtained in-vivo datasets. The results, encompassing both qualitative and quantitative analyses, showcase that the proposed method attains state-of-the-art performance in DTI parameter estimation. Notably, it demonstrates superior generalization, accuracy, and efficiency, rendering it highly reliable for widespread application in the field.
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